Overview

Dataset statistics

Number of variables18
Number of observations525
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.0 KiB
Average record size in memory144.2 B

Variable types

Numeric8
Categorical6
Boolean4

Alerts

Certificate has constant value "True" Constant
Trainer_experiance is highly correlated with Course_hours and 6 other fieldsHigh correlation
Course_hours is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Course_rating is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Rental_permises is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Trainer_slary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Price is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Trainer_experiance is highly correlated with Course_hours and 6 other fieldsHigh correlation
Course_hours is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Course_rating is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Rental_permises is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Trainer_slary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Price is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Trainer_experiance is highly correlated with Course_hours and 6 other fieldsHigh correlation
Course_hours is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Course_rating is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Rental_permises is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Trainer_slary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Maintaince_cost is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Non_teaching_staff_salary is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Price is highly correlated with Trainer_experiance and 6 other fieldsHigh correlation
Certificate is highly correlated with Location and 8 other fieldsHigh correlation
Location is highly correlated with CertificateHigh correlation
Offline_classes is highly correlated with CertificateHigh correlation
Course_rating is highly correlated with CertificateHigh correlation
Subject is highly correlated with CertificateHigh correlation
Placements is highly correlated with CertificateHigh correlation
Online_classes is highly correlated with CertificateHigh correlation
Institute is highly correlated with CertificateHigh correlation
Course_level is highly correlated with CertificateHigh correlation
Trainer_Qualification is highly correlated with CertificateHigh correlation
Course_hours is highly correlated with Course_rating and 6 other fieldsHigh correlation
Course_rating is highly correlated with Course_hours and 6 other fieldsHigh correlation
Maintaince_cost is highly correlated with Course_hours and 6 other fieldsHigh correlation
Non_teaching_staff_salary is highly correlated with Course_hours and 6 other fieldsHigh correlation
Price is highly correlated with Course_hours and 6 other fieldsHigh correlation
Rental_permises is highly correlated with Course_hours and 6 other fieldsHigh correlation
Trainer_experiance is highly correlated with Course_hours and 6 other fieldsHigh correlation
Trainer_slary is highly correlated with Course_hours and 6 other fieldsHigh correlation
s.no is uniformly distributed Uniform
s.no has unique values Unique

Reproduction

Analysis started2022-07-01 05:03:37.752292
Analysis finished2022-07-01 05:03:46.381936
Duration8.63 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

s.no
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct525
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263
Minimum1
Maximum525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:46.455588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.2
Q1132
median263
Q3394
95-th percentile498.8
Maximum525
Range524
Interquartile range (IQR)262

Descriptive statistics

Standard deviation151.6987146
Coefficient of variation (CV)0.5768011961
Kurtosis-1.2
Mean263
Median Absolute Deviation (MAD)131
Skewness0
Sum138075
Variance23012.5
MonotonicityStrictly increasing
2022-07-01T10:33:46.586952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
3621
 
0.2%
3601
 
0.2%
3591
 
0.2%
3581
 
0.2%
3571
 
0.2%
3561
 
0.2%
3551
 
0.2%
3541
 
0.2%
3531
 
0.2%
Other values (515)515
98.1%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
5251
0.2%
5241
0.2%
5231
0.2%
5221
0.2%
5211
0.2%
5201
0.2%
5191
0.2%
5181
0.2%
5171
0.2%
5161
0.2%

Institute
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
innomatics
49 
Great_Learn
46 
Coursera
45 
Datatrain
41 
Upgrad
41 
Other values (9)
303 

Length

Max length12
Median length9
Mean length7.866666667
Min length3

Characters and Unicode

Total characters4130
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDatatrain
2nd rowDatatrain
3rd rowGuvi
4th rowinnomatics
5th rowDatatrain

Common Values

ValueCountFrequency (%)
innomatics49
 
9.3%
Great_Learn46
 
8.8%
Coursera45
 
8.6%
Datatrain41
 
7.8%
Upgrad41
 
7.8%
ExcelR40
 
7.6%
Simple_learn36
 
6.9%
Udemy35
 
6.7%
Edvancer34
 
6.5%
Edureka33
 
6.3%
Other values (4)125
23.8%

Length

2022-07-01T10:33:46.694697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
innomatics49
 
9.3%
great_learn46
 
8.8%
coursera45
 
8.6%
datatrain41
 
7.8%
upgrad41
 
7.8%
excelr40
 
7.6%
simple_learn36
 
6.9%
udemy35
 
6.7%
edvancer34
 
6.5%
edureka33
 
6.3%
Other values (4)125
23.8%

Most occurring characters

ValueCountFrequency (%)
a549
 
13.3%
r367
 
8.9%
e351
 
8.5%
i267
 
6.5%
n255
 
6.2%
t209
 
5.1%
d174
 
4.2%
c155
 
3.8%
m152
 
3.7%
E138
 
3.3%
Other values (23)1513
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3274
79.3%
Uppercase Letter652
 
15.8%
Connector Punctuation114
 
2.8%
Decimal Number90
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a549
16.8%
r367
11.2%
e351
10.7%
i267
 
8.2%
n255
 
7.8%
t209
 
6.4%
d174
 
5.3%
c155
 
4.7%
m152
 
4.6%
l112
 
3.4%
Other values (9)683
20.9%
Uppercase Letter
ValueCountFrequency (%)
E138
21.2%
G108
16.6%
D103
15.8%
U76
11.7%
L46
 
7.1%
C45
 
6.9%
R40
 
6.1%
S36
 
5.5%
T30
 
4.6%
M30
 
4.6%
Decimal Number
ValueCountFrequency (%)
330
33.3%
630
33.3%
030
33.3%
Connector Punctuation
ValueCountFrequency (%)
_114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3926
95.1%
Common204
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a549
14.0%
r367
 
9.3%
e351
 
8.9%
i267
 
6.8%
n255
 
6.5%
t209
 
5.3%
d174
 
4.4%
c155
 
3.9%
m152
 
3.9%
E138
 
3.5%
Other values (19)1309
33.3%
Common
ValueCountFrequency (%)
_114
55.9%
330
 
14.7%
630
 
14.7%
030
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a549
 
13.3%
r367
 
8.9%
e351
 
8.5%
i267
 
6.5%
n255
 
6.2%
t209
 
5.1%
d174
 
4.2%
c155
 
3.8%
m152
 
3.7%
E138
 
3.3%
Other values (23)1513
36.6%

Subject
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
ProjectManagement
62 
python
60 
DataScience
58 
ArtificialIntelligence
53 
FullstackDataScience
53 
Other values (5)
239 

Length

Max length22
Median length16
Mean length14.44571429
Min length6

Characters and Unicode

Total characters7584
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProjectManagement
2nd rowDataScience
3rd rowpython
4th rowDigitalTransformation
5th rowDigitalTransformation

Common Values

ValueCountFrequency (%)
ProjectManagement62
11.8%
python60
11.4%
DataScience58
11.0%
ArtificialIntelligence53
10.1%
FullstackDataScience53
10.1%
BigData52
9.9%
CloudComputing52
9.9%
DigitalTransformation46
8.8%
DigitalMarketing45
8.6%
Dataanalysis44
8.4%

Length

2022-07-01T10:33:46.794350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T10:33:46.915734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
projectmanagement62
11.8%
python60
11.4%
datascience58
11.0%
artificialintelligence53
10.1%
fullstackdatascience53
10.1%
bigdata52
9.9%
cloudcomputing52
9.9%
digitaltransformation46
8.8%
digitalmarketing45
8.6%
dataanalysis44
8.4%

Most occurring characters

ValueCountFrequency (%)
a960
12.7%
t784
 
10.3%
i744
 
9.8%
n634
 
8.4%
e612
 
8.1%
l452
 
6.0%
c443
 
5.8%
g355
 
4.7%
o318
 
4.2%
D298
 
3.9%
Other values (20)1984
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6645
87.6%
Uppercase Letter939
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a960
14.4%
t784
11.8%
i744
11.2%
n634
9.5%
e612
9.2%
l452
6.8%
c443
6.7%
g355
 
5.3%
o318
 
4.8%
r252
 
3.8%
Other values (10)1091
16.4%
Uppercase Letter
ValueCountFrequency (%)
D298
31.7%
S111
 
11.8%
M107
 
11.4%
C104
 
11.1%
P62
 
6.6%
F53
 
5.6%
I53
 
5.6%
A53
 
5.6%
B52
 
5.5%
T46
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin7584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a960
12.7%
t784
 
10.3%
i744
 
9.8%
n634
 
8.4%
e612
 
8.1%
l452
 
6.0%
c443
 
5.8%
g355
 
4.7%
o318
 
4.2%
D298
 
3.9%
Other values (20)1984
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a960
12.7%
t784
 
10.3%
i744
 
9.8%
n634
 
8.4%
e612
 
8.1%
l452
 
6.0%
c443
 
5.8%
g355
 
4.7%
o318
 
4.2%
D298
 
3.9%
Other values (20)1984
26.2%

Location
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Bengaluru
65 
Hyderabad
63 
Chennai
61 
Ahmedabad
61 
Pune
61 
Other values (4)
214 

Length

Max length9
Median length8
Mean length7.161904762
Min length4

Characters and Unicode

Total characters3760
Distinct characters25
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowHyderabad
3rd rowChennai
4th rowAhmedabad
5th rowPune

Common Values

ValueCountFrequency (%)
Bengaluru65
12.4%
Hyderabad63
12.0%
Chennai61
11.6%
Ahmedabad61
11.6%
Pune61
11.6%
Delhi57
10.9%
Calcutta54
10.3%
Kanpour53
10.1%
Mumbai50
9.5%

Length

2022-07-01T10:33:47.039660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T10:33:47.148062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
bengaluru65
12.4%
hyderabad63
12.0%
chennai61
11.6%
ahmedabad61
11.6%
pune61
11.6%
delhi57
10.9%
calcutta54
10.3%
kanpour53
10.1%
mumbai50
9.5%

Most occurring characters

ValueCountFrequency (%)
a585
15.6%
e368
 
9.8%
u348
 
9.3%
n301
 
8.0%
d248
 
6.6%
r181
 
4.8%
h179
 
4.8%
l176
 
4.7%
b174
 
4.6%
i168
 
4.5%
Other values (15)1032
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3235
86.0%
Uppercase Letter525
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a585
18.1%
e368
11.4%
u348
10.8%
n301
9.3%
d248
7.7%
r181
 
5.6%
h179
 
5.5%
l176
 
5.4%
b174
 
5.4%
i168
 
5.2%
Other values (7)507
15.7%
Uppercase Letter
ValueCountFrequency (%)
C115
21.9%
B65
12.4%
H63
12.0%
A61
11.6%
P61
11.6%
D57
10.9%
K53
10.1%
M50
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a585
15.6%
e368
 
9.8%
u348
 
9.3%
n301
 
8.0%
d248
 
6.6%
r181
 
4.8%
h179
 
4.8%
l176
 
4.7%
b174
 
4.6%
i168
 
4.5%
Other values (15)1032
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a585
15.6%
e368
 
9.8%
u348
 
9.3%
n301
 
8.0%
d248
 
6.6%
r181
 
4.8%
h179
 
4.8%
l176
 
4.7%
b174
 
4.6%
i168
 
4.5%
Other values (15)1032
27.4%

Trainer_Qualification
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Graduate
184 
Doctorate
178 
UnderGraduate
163 

Length

Max length13
Median length9
Mean length9.891428571
Min length8

Characters and Unicode

Total characters5193
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDoctorate
2nd rowGraduate
3rd rowUnderGraduate
4th rowDoctorate
5th rowUnderGraduate

Common Values

ValueCountFrequency (%)
Graduate184
35.0%
Doctorate178
33.9%
UnderGraduate163
31.0%

Length

2022-07-01T10:33:47.257761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T10:33:47.346167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
graduate184
35.0%
doctorate178
33.9%
undergraduate163
31.0%

Most occurring characters

ValueCountFrequency (%)
a872
16.8%
t703
13.5%
r688
13.2%
e688
13.2%
d510
9.8%
o356
6.9%
G347
 
6.7%
u347
 
6.7%
D178
 
3.4%
c178
 
3.4%
Other values (2)326
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4505
86.8%
Uppercase Letter688
 
13.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a872
19.4%
t703
15.6%
r688
15.3%
e688
15.3%
d510
11.3%
o356
7.9%
u347
 
7.7%
c178
 
4.0%
n163
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
G347
50.4%
D178
25.9%
U163
23.7%

Most occurring scripts

ValueCountFrequency (%)
Latin5193
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a872
16.8%
t703
13.5%
r688
13.2%
e688
13.2%
d510
9.8%
o356
6.9%
G347
 
6.7%
u347
 
6.7%
D178
 
3.4%
c178
 
3.4%
Other values (2)326
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a872
16.8%
t703
13.5%
r688
13.2%
e688
13.2%
d510
9.8%
o356
6.9%
G347
 
6.7%
u347
 
6.7%
D178
 
3.4%
c178
 
3.4%
Other values (2)326
 
6.3%

Online_classes
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size653.0 B
True
290 
False
235 
ValueCountFrequency (%)
True290
55.2%
False235
44.8%
2022-07-01T10:33:47.616942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Offline_classes
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size653.0 B
True
275 
False
250 
ValueCountFrequency (%)
True275
52.4%
False250
47.6%
2022-07-01T10:33:47.693496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Course_level
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
intermediate
181 
beginer
180 
advanced
164 

Length

Max length12
Median length8
Mean length9.036190476
Min length7

Characters and Unicode

Total characters4744
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadvanced
2nd rowadvanced
3rd rowadvanced
4th rowintermediate
5th rowadvanced

Common Values

ValueCountFrequency (%)
intermediate181
34.5%
beginer180
34.3%
advanced164
31.2%

Length

2022-07-01T10:33:47.771400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T10:33:47.869714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
intermediate181
34.5%
beginer180
34.3%
advanced164
31.2%

Most occurring characters

ValueCountFrequency (%)
e1067
22.5%
i542
11.4%
n525
11.1%
d509
10.7%
a509
10.7%
t362
 
7.6%
r361
 
7.6%
m181
 
3.8%
b180
 
3.8%
g180
 
3.8%
Other values (2)328
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4744
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1067
22.5%
i542
11.4%
n525
11.1%
d509
10.7%
a509
10.7%
t362
 
7.6%
r361
 
7.6%
m181
 
3.8%
b180
 
3.8%
g180
 
3.8%
Other values (2)328
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin4744
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1067
22.5%
i542
11.4%
n525
11.1%
d509
10.7%
a509
10.7%
t362
 
7.6%
r361
 
7.6%
m181
 
3.8%
b180
 
3.8%
g180
 
3.8%
Other values (2)328
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1067
22.5%
i542
11.4%
n525
11.1%
d509
10.7%
a509
10.7%
t362
 
7.6%
r361
 
7.6%
m181
 
3.8%
b180
 
3.8%
g180
 
3.8%
Other values (2)328
 
6.9%

Placements
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size653.0 B
False
269 
True
256 
ValueCountFrequency (%)
False269
51.2%
True256
48.8%
2022-07-01T10:33:47.958800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Certificate
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size653.0 B
True
525 
ValueCountFrequency (%)
True525
100.0%
2022-07-01T10:33:48.038781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Trainer_experiance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.79047619
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:48.105267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.515929808
Coefficient of variation (CV)0.5111850219
Kurtosis-1.07758173
Mean10.79047619
Median Absolute Deviation (MAD)5
Skewness-0.07460615264
Sum5665
Variance30.42548164
MonotonicityNot monotonic
2022-07-01T10:33:48.194139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1342
 
8.0%
1142
 
8.0%
638
 
7.2%
1928
 
5.3%
1828
 
5.3%
928
 
5.3%
1627
 
5.1%
1227
 
5.1%
1727
 
5.1%
1026
 
5.0%
Other values (10)212
40.4%
ValueCountFrequency (%)
122
4.2%
224
4.6%
319
3.6%
417
3.2%
525
4.8%
638
7.2%
722
4.2%
820
3.8%
928
5.3%
1026
5.0%
ValueCountFrequency (%)
2021
4.0%
1928
5.3%
1828
5.3%
1727
5.1%
1627
5.1%
1524
4.6%
1418
3.4%
1342
8.0%
1227
5.1%
1142
8.0%

Course_hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct150
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.5638095
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:48.305241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile47
Q178
median118
Q3165
95-th percentile192
Maximum200
Range160
Interquartile range (IQR)87

Descriptive statistics

Standard deviation47.49260916
Coefficient of variation (CV)0.3972155901
Kurtosis-1.217647116
Mean119.5638095
Median Absolute Deviation (MAD)43
Skewness0.03513412236
Sum62771
Variance2255.547924
MonotonicityNot monotonic
2022-07-01T10:33:48.423724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1279
 
1.7%
429
 
1.7%
518
 
1.5%
1828
 
1.5%
1788
 
1.5%
1187
 
1.3%
627
 
1.3%
747
 
1.3%
977
 
1.3%
1097
 
1.3%
Other values (140)448
85.3%
ValueCountFrequency (%)
402
 
0.4%
413
 
0.6%
429
1.7%
434
0.8%
443
 
0.6%
451
 
0.2%
463
 
0.6%
474
0.8%
483
 
0.6%
495
1.0%
ValueCountFrequency (%)
2003
0.6%
1993
0.6%
1983
0.6%
1971
 
0.2%
1967
1.3%
1953
0.6%
1943
0.6%
1932
 
0.4%
1926
1.1%
1916
1.1%

Course_rating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
3
150 
2
120 
4
120 
1
78 
5
57 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters525
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row2
3rd row4
4th row5
5th row2

Common Values

ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Length

2022-07-01T10:33:48.530473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-01T10:33:48.625216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Most occurring characters

ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number525
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common525
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3150
28.6%
2120
22.9%
4120
22.9%
178
14.9%
557
 
10.9%

Rental_permises
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct134
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.63238095
Minimum15
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:48.725889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q149
median84
Q3118
95-th percentile142.8
Maximum150
Range135
Interquartile range (IQR)69

Descriptive statistics

Standard deviation39.1478907
Coefficient of variation (CV)0.4737596842
Kurtosis-1.145711474
Mean82.63238095
Median Absolute Deviation (MAD)34
Skewness-0.03454047398
Sum43382
Variance1532.557346
MonotonicityNot monotonic
2022-07-01T10:33:48.851202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8412
 
2.3%
13110
 
1.9%
1610
 
1.9%
759
 
1.7%
1418
 
1.5%
958
 
1.5%
348
 
1.5%
788
 
1.5%
628
 
1.5%
907
 
1.3%
Other values (124)437
83.2%
ValueCountFrequency (%)
155
1.0%
1610
1.9%
177
1.3%
183
 
0.6%
191
 
0.2%
202
 
0.4%
214
 
0.8%
224
 
0.8%
232
 
0.4%
247
1.3%
ValueCountFrequency (%)
1502
 
0.4%
1493
 
0.6%
1484
0.8%
1474
0.8%
1463
 
0.6%
1457
1.3%
1442
 
0.4%
1432
 
0.4%
1422
 
0.4%
1418
1.5%

Trainer_slary
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct313
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.112381
Minimum200
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:48.971016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile230
Q1354
median515
Q3653
95-th percentile773.8
Maximum800
Range600
Interquartile range (IQR)299

Descriptive statistics

Standard deviation174.4551298
Coefficient of variation (CV)0.3446964278
Kurtosis-1.164841365
Mean506.112381
Median Absolute Deviation (MAD)155
Skewness-0.05501038188
Sum265709
Variance30434.59231
MonotonicityNot monotonic
2022-07-01T10:33:49.085296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5118
 
1.5%
2466
 
1.1%
2906
 
1.1%
7205
 
1.0%
5204
 
0.8%
2304
 
0.8%
2804
 
0.8%
5394
 
0.8%
5454
 
0.8%
2104
 
0.8%
Other values (303)476
90.7%
ValueCountFrequency (%)
2001
 
0.2%
2021
 
0.2%
2042
0.4%
2051
 
0.2%
2071
 
0.2%
2081
 
0.2%
2091
 
0.2%
2104
0.8%
2111
 
0.2%
2121
 
0.2%
ValueCountFrequency (%)
8003
0.6%
7992
0.4%
7981
 
0.2%
7971
 
0.2%
7963
0.6%
7952
0.4%
7942
0.4%
7911
 
0.2%
7892
0.4%
7872
0.4%

Maintaince_cost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct132
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.02095238
Minimum15
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:49.205179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20.2
Q151
median85
Q3119
95-th percentile143
Maximum150
Range135
Interquartile range (IQR)68

Descriptive statistics

Standard deviation38.97583832
Coefficient of variation (CV)0.4638823676
Kurtosis-1.117248825
Mean84.02095238
Median Absolute Deviation (MAD)34
Skewness-0.05983190389
Sum44111
Variance1519.115972
MonotonicityNot monotonic
2022-07-01T10:33:49.327854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13211
 
2.1%
1611
 
2.1%
359
 
1.7%
259
 
1.7%
769
 
1.7%
968
 
1.5%
478
 
1.5%
808
 
1.5%
638
 
1.5%
957
 
1.3%
Other values (122)437
83.2%
ValueCountFrequency (%)
151
 
0.2%
1611
2.1%
174
 
0.8%
185
1.0%
193
 
0.6%
203
 
0.6%
213
 
0.6%
222
 
0.4%
234
 
0.8%
241
 
0.2%
ValueCountFrequency (%)
1503
0.6%
1497
1.3%
1484
0.8%
1473
0.6%
1464
0.8%
1453
0.6%
1442
 
0.4%
1434
0.8%
1427
1.3%
1414
0.8%

Non_teaching_staff_salary
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.6152381
Minimum50
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:49.449403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile59
Q194
median137
Q3181
95-th percentile217
Maximum225
Range175
Interquartile range (IQR)87

Descriptive statistics

Standard deviation50.66824965
Coefficient of variation (CV)0.3708828558
Kurtosis-1.133297419
Mean136.6152381
Median Absolute Deviation (MAD)44
Skewness0.02271543259
Sum71723
Variance2567.271523
MonotonicityNot monotonic
2022-07-01T10:33:49.566003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
758
 
1.5%
1467
 
1.3%
2247
 
1.3%
527
 
1.3%
727
 
1.3%
1517
 
1.3%
637
 
1.3%
1277
 
1.3%
956
 
1.1%
1166
 
1.1%
Other values (154)456
86.9%
ValueCountFrequency (%)
502
 
0.4%
514
0.8%
527
1.3%
532
 
0.4%
544
0.8%
552
 
0.4%
561
 
0.2%
572
 
0.4%
582
 
0.4%
594
0.8%
ValueCountFrequency (%)
2254
0.8%
2247
1.3%
2233
0.6%
2221
 
0.2%
2214
0.8%
2202
 
0.4%
2194
0.8%
2173
0.6%
2161
 
0.2%
2155
1.0%

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct428
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean895.992381
Minimum302
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2022-07-01T10:33:49.688523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum302
5-th percentile363.2
Q1602
median889
Q31192
95-th percentile1445.4
Maximum1499
Range1197
Interquartile range (IQR)590

Descriptive statistics

Standard deviation343.817128
Coefficient of variation (CV)0.3837277362
Kurtosis-1.097880353
Mean895.992381
Median Absolute Deviation (MAD)291
Skewness0.03731601394
Sum470396
Variance118210.2175
MonotonicityNot monotonic
2022-07-01T10:33:49.809253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14955
 
1.0%
4744
 
0.8%
11674
 
0.8%
7963
 
0.6%
7453
 
0.6%
9463
 
0.6%
9323
 
0.6%
3203
 
0.6%
9283
 
0.6%
13183
 
0.6%
Other values (418)491
93.5%
ValueCountFrequency (%)
3021
 
0.2%
3061
 
0.2%
3072
0.4%
3082
0.4%
3101
 
0.2%
3111
 
0.2%
3181
 
0.2%
3191
 
0.2%
3203
0.6%
3221
 
0.2%
ValueCountFrequency (%)
14991
 
0.2%
14981
 
0.2%
14971
 
0.2%
14962
 
0.4%
14955
1.0%
14941
 
0.2%
14921
 
0.2%
14871
 
0.2%
14841
 
0.2%
14821
 
0.2%

Interactions

2022-07-01T10:33:45.128357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.066123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.789392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.933622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.744234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.530405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.440776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.340346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.220773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.150450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.880968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.040652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.835289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.628673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.669621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.426013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.322562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.242301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.315664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.144449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.935467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.731730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.776927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.523724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.425539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.336601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.417517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.244282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.040519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.828598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.880214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.624599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.517537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.426522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.519253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.349208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.136987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.926875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.978815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.719362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.613377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.516348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.621737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.451364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.232549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.024858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.063907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.826053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.708217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.606015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.721464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.547946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.329056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.172269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.155253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.924444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.805158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:39.699014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:40.827882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:41.648057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:42.428840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:43.337223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:44.252012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-01T10:33:45.027023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-01T10:33:49.907508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-01T10:33:50.030649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-01T10:33:50.154121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-01T10:33:50.278342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-01T10:33:50.408851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-01T10:33:45.994857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-01T10:33:46.274563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

s.noInstituteSubjectLocationTrainer_QualificationOnline_classesOffline_classesCourse_levelPlacementsCertificateTrainer_experianceCourse_hoursCourse_ratingRental_permisesTrainer_slaryMaintaince_costNon_teaching_staff_salaryPrice
01DatatrainProjectManagementDelhiDoctorateYesYesadvancedYesYes1919251457221402051419
12DatatrainDataScienceHyderabadGraduateYesnoadvancedNoYes99426845470116746
23GuvipythonChennaiUnderGraduatenonoadvancedYesYes1515441126411181791121
34innomaticsDigitalTransformationAhmedabadDoctorateYesYesintermediateNoYes1818251307291321081341
45DatatrainDigitalTransformationPuneUnderGraduateYesYesadvancedNoYes6752433494588512
56DatatrainpythonAhmedabadUnderGraduatenoYesbeginerYesYes1111538451080135879
67360DigiTMGBigDataPuneUnderGraduatenonobeginerYesYes6632372974076429
78EdurekaArtificialIntelligenceKanpourDoctoratenoYesadvancedYesYes6702423154680481
89UdemyBigDataBengaluruDoctoratenonobeginerYesYes6782513565195618
910CourseraDataanalysisKanpourGraduateYesYesadvancedYesYes6672393104378463

Last rows

s.noInstituteSubjectLocationTrainer_QualificationOnline_classesOffline_classesCourse_levelPlacementsCertificateTrainer_experianceCourse_hoursCourse_ratingRental_permisesTrainer_slaryMaintaince_costNon_teaching_staff_salaryPrice
515516GuviBigDataPuneDoctoratenonoadvancedYesYes2491222382361377
516517innomaticsDigitalMarketingKanpourUnderGraduateYesnoadvancedNoYes1313139254494150977
517518Data_campDataanalysisHyderabadUnderGraduateYesnobeginerNoYes1717841317141321991318
518519Data_campDataScienceChennaiDoctoratenoYesbeginerYesYes1718041357231342021336
519520innomaticsProjectManagementPuneGraduateYesnointermediateNoYes1818451387381372071371
520521EdurekaCloudComputingDelhiGraduatenoYesintermediateYesYes4661292693069435
521522EdxFullstackDataScienceHyderabadDoctoratenonobeginerYesYes2019551477841462201462
522523ExcelRDigitalTransformationDelhiGraduatenoYesintermediateYesYes1421162121695601
523524DatatrainCloudComputingPuneUnderGraduatenonobeginerNoYes5681312753271446
524525GuviCloudComputingDelhiGraduateYesYesbeginerYesYes1516241216551201821187